# Leveraging Cross-Lingual Transfer Learning in Spoken Named Entity Recognition Systems

arXiv: https://arxiv.org/abs/2307.01310 · HTML: https://mlsanity.com/paper/2307.01310/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 3 Jul 2023.

## Builds on (most cited first, 12 of 12)

- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [Towards end-to-end spoken language understanding](https://mlsanity.com/paper/1802.08395/) (2018)
- [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://mlsanity.com/paper/2105.00572/) (2021)
- [SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech](https://mlsanity.com/paper/2111.10367/) (2021)
- [From Audio to Semantics: Approaches to end-to-end spoken language understanding](https://mlsanity.com/paper/1809.09190/) (2018)
- [End-to-end Named Entity Recognition from English Speech](https://mlsanity.com/paper/2005.11184/) (2020)
- [AISHELL-NER: Named Entity Recognition from Chinese Speech](https://mlsanity.com/paper/2202.08533/) (2022)
- [Audio De-identification: A New Entity Recognition Task](https://mlsanity.com/paper/1903.07037/) (2019)
- [Recent Advances in End-to-End Spoken Language Understanding](https://mlsanity.com/paper/1909.13332/) (2019)
- [End-to-end model for named entity recognition from speech without paired training data](https://mlsanity.com/paper/2204.00803/) (2022)
- [What do we Really Know about State of the Art NER?](https://mlsanity.com/paper/2205.00034/) (2022)

## Abstract (arXiv)

Recent Named Entity Recognition (NER) advancements have significantly enhanced text classification capabilities. This paper focuses on spoken NER, aimed explicitly at spoken document retrieval, an area not widely studied due to the lack of comprehensive datasets for spoken contexts. Additionally, the potential for cross-lingual transfer learning in low-resource situations deserves further investigation. In our study, we applied transfer learning techniques across Dutch, English, and German using both pipeline and End-to-End (E2E) approaches. We employed Wav2Vec2 XLS-R models on custom pseudo-annotated datasets to evaluate the adaptability of cross-lingual systems. Our exploration of different architectural configurations assessed the robustness of these systems in spoken NER. Results showed that the E2E model was superior to the pipeline model, particularly with limited annotation resources. Furthermore, transfer learning from German to Dutch improved performance by 7% over the standalone Dutch E2E system and 4% over the Dutch pipeline model. Our findings highlight the effectiveness of cross-lingual transfer in spoken NER and emphasize the need for additional data collection to improve these systems.